Biology subjects

Craighero, F.

Publications and source records attributed to Craighero, F..

3 recordsLinked to original sources

bioRxiv2025

Conditional Deep Learning Model Reveals Translation Elongation Determinants during Amino Acid Deprivation

Translation elongation plays a key role in cellular homeostasis, and dysregulation of this process has been implicated in various diseases and metabolic disorders. Uncovering the causes of intragenic heterogeneity of translation, especially in contexts of different amino acid deprivations, could help increase our understanding of these disorders and pave the way for novel therapeutics. Ribosome profiling provides accurate measurements for the genome-wide ribosome footprints, which could be utilized to investigate these mechanisms. Here we present Riboclette, a conditional deep learning model featuring a dual output head that uses the mRNA sequence input to accurately predict the ribosome footprint profiles across six amino acid deprivation conditions. Exploiting standard interpretability methods, we identify specific codons related to deprived amino acids, poly-basic regions, and negatively charged amino acids as the primary drivers of the stalling response. Moreover, we use Riboclette to extract motif level drivers for ribosome stalling by performing in silico perturbation experiments. The extracted motifs highlight the cumulative contribution of deprived codons in triggering ribosome stalling, which, depending on the condition, have been identified to affect translation up to ten codons downstream. Moreover, motifs precisely explain stalling at different codon positions, allowing for the differentiation between expected determinants of rare stalling events. Our framework offers an accurate and explainable method for understanding the effect of different amino acid deprivation conditions at a codon resolution to help elucidate the impact of intragenic variations on the regulation of translation elongation.

bioinformatics↗

bioRxiv2024

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, and in turn, predict the speed of translation. The proposed method, RiboGL, combines graph and recurrent neural networks to account for both graph and sequence-based features. The model takes a graph representing the secondary structure of the mRNA sequence as input, which incorporates both sequence and structural codon neighbors. In our experiments, RiboGL greatly outperforms the state-of-the-art RiboMIMO model for ribosome density prediction. We also conduct ablation studies to justify the design choices made in building the pipeline. Additionally, we use gradient-based interpretability to understand how the codon context and the structural neighbors affect the ribosome density at the A-site. By individually analyzing the genes in the dataset, we elucidate how structural neighbors could also potentially play a role in defining the ribosome density. Importantly, since these neighbors can be far away in the sequence, a recurrent model alone could not easily extract this information. This study lays the foundation for understanding how the mRNA secondary structure can be exploited for ribosome density prediction, and how in the future other graph modalities such as features from the nascent polypeptide can be used to further our understanding of translation in general.

bioinformatics↗

bioRxiv2023

Interpretable Inflammation Landscape of Circulating Immune cells

Inflammation is a biological phenomenon involved in a wide variety of physiological and pathological processes. Although a controlled inflammatory response is beneficial for restoring homeostasis, it can become unfavorable if dysregulated. In recent years, major progress has been made in characterizing acute and chronic inflammation in specific diseases. However, a global, holistic understanding of inflammation is still elusive. This is particularly intriguing, considering the crucial function of inflammation for human health and its potential for modern medicine if fully deciphered. Here, we leverage advances in the field of single-cell genomics to delineate the full spectrum of circulating immune cell activation underlying inflammatory processes during infection, immune-mediated inflammatory diseases and cancer. Our single-cell atlas of >6.5 million peripheral blood mononuclear cells from 1047 patients and 19 diseases allowed us to learn a comprehensive model of inflammation in circulating immune cells. The atlas expanded our current knowledge of the biology of inflammation of immune-mediated diseases, acute and chronic inflammatory diseases, infection and solid tumors, and laid the foundation to develop a precision medicine framework using unsupervised as well as explainable machine learning. Beyond a disease-centered analysis, we charted altered activity of inflammatory molecules in peripheral blood cells, depicting discriminative inflammation-related genes to further understand mechanisms of inflammation. Finally, we have laid the groundwork for developing precision medicine diagnostic tools for patients experiencing pathologic inflammation by learning a classifier for inflammatory diseases, presenting cells in circulation as a powerful resource for patient diagnosis.

immunology↗